class SearchEngine:
# DO NOT MODIFY THIS SIGNATURE
# You can change the internal implementation, but you must have a parser and an indexer.
def __init__(self, config=None):
if not config:
self._config = ConfigClass()
else:
self._config = config
self._parser = Parse()
self._indexer = Indexer(self._config)
self._model = None
self._reader = ReadFile(self._config.get__corpusPath())
# DO NOT MODIFY THIS SIGNATURE
# You can change the internal implmentation as you see fit.
def build_index_from_parquet(self, fn):
"""
Reads parquet file and passes it to the parser, then indexer.
Input:
fn - path to parquet file
Output:
No output, just modifies the internal _indexer object.
"""
df = pd.read_parquet(fn, engine="pyarrow")
documents_list = df.values.tolist()
# Iterate over every document in the file
for idx, document in enumerate(documents_list):
# parse the document
parsed_document = self._parser.parse_doc(document)
# index the document data
self._indexer.add_new_doc(parsed_document)
self._indexer.check_pending_list()
self._indexer.calculate_and_add_idf()
self._indexer.calculate_sigma_Wij()
self._indexer.calculate_avg_doc_len()
# DO NOT MODIFY THIS SIGNATURE
# You can change the internal implmentation as you see fit.
def load_index(self, fn):
"""
Loads a pre-computed index (or indices) so we can answer queries.
Input:
fn - file name of pickled index.
"""
self._indexer.load_index(fn)
# DO NOT MODIFY THIS SIGNATURE
# You can change the internal implmentation as you see fit.
def load_precomputed_model(self, model_dir=None):
"""
Loads a pre-computed model (or models) so we can answer queries.
This is where you would load models like word2vec, LSI, LDA, etc. and
assign to self._model, which is passed on to the searcher at query time.
"""
pass
def search(self, query, k=None):
"""
Executes a query over an existing index and returns the number of
relevant docs and an ordered list of search results.
Input:
query - string.
Output:
A tuple containing the number of relevant search results, and
a list of tweet_ids where the first element is the most relavant
and the last is the least relevant result.
"""
query_as_tuple = self._parser.parse_sentence(query)
query_as_list = query_as_tuple[0] + query_as_tuple[1]
searcher = Searcher(self._parser, self._indexer, model=self._model)
return searcher.search(query_as_list, k)
